Zadka Assaf, Rabin Neta, Gazit Eran, Mirelman Anat, Nieuwboer Alice, Rochester Lynn, Del Din Silvia, Pelosin Elisa, Avanzino Laura, Bloem Bastiaan R, Della Croce Ugo, Cereatti Andrea, Hausdorff Jeffrey M
Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.
Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.
NPJ Digit Med. 2024 May 25;7(1):142. doi: 10.1038/s41746-024-01136-2.
Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson's disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.
步长是健康和疾病的一项重要诊断及预后指标。可穿戴设备能够持续估算步长（例如在诊所或现实环境中），然而，当前估算方法的准确性尚未达到最佳状态。我们基于472名患有不同神经系统疾病（包括帕金森病）的年轻人和老年人以及健康对照者佩戴的单个下背部惯性测量单元所获取的数据，开发了用于估算步长的机器学习模型。在研究超过80000步的过程中，最佳模型对单步显示出较高的准确性（均方根误差，RMSE = 6.08厘米，组内相关系数ICC(2,1)=0.89），并且在连续十步平均时准确性更高（RMSE = 4.79厘米，ICC(2,1)=0.93），成功达到了RMSE低于5厘米的预定义目标（通常被认为是最小临床重要差异）。即使在患有神经系统疾病的患者中，将机器学习与单个可穿戴传感器相结合也能生成准确的步长测量值。可能需要进一步的研究来在某些情况下进一步减少误差。
